Effects of turbulence on diatoms of the genus Pseudo-nitzschia spp. and associated bacteria

Abstract Turbulence is one of the least investigated environmental factors impacting the ecophysiology of phytoplankton, both at the community and individual species level. Here, we investigated, for the first time, the effect of a turbulence gradient (Reynolds number, from Reλ = 0 to Reλ = 360) on two species of the marine diatom Pseudo-nitzschia and their associated bacterial communities under laboratory conditions. Cell abundance, domoic acid (DA) production, chain formation, and Chl a content of P. fraudulenta and P. multiseries were higher for intermediate turbulence (Reλ = 160 or 240). DA was detectable only in P. multiseries samples. These observations were supported by transcriptomic analyses results, which suggested the turbulence related induction of the expression of the DA production locus, with a linkage to an increased photosynthetic activity of the total metatranscriptome. This study also highlighted a higher richness of the bacterial community associated with the nontoxic strain of P. fraudulenta in comparison to the toxic strain of P. multiseries. Bacillus was an important genus in P. multiseries cultures (relative abundance 15.5%) and its highest abundances coincided with the highest DA levels. However, associated bacterial communities of both Pseudo-nitzschia species did not show clear patterns relative to turbulence intensity.


Introduction
Turbulence in the oceans, generated by various factors like winds, cooling, e v a por ation, tides (Thor pe 2005 ), and wav es, has a profound impact on marine phytoplankton (e.g.Estrada andBerdalet 1997 , Sc hmitt 2020 ).In thr ee-dimensional turbulence, ther e is a cascade of eddies from large to small scales, until the Kolmogorov scale where viscous dissipation becomes dominant, which is of the order of 1000 μm in the ocean's surface waters.P assiv e scalars, such as temperature, salinity, or the concentration in nutrients, ar e tr ansported by turbulence; their fluctuations ar e gener ated at large scales and transported through successive breakdowns into smaller scales until the Batc helor scale, wher e the effect of molecular diffusion become important in comparison to turbulent mixing (Batchelor 1959 ).The Batchelor scale is of the order of 10-100 μm in the ocean's surface waters .Diatoms , r anging fr om 2 to 200 μm, have a size similar to the Batchelor scale and are m uc h smaller than the K olmogoro v scale .T his means that these phytoplankton cells experience laminar shears due to turbulence (Estrada andBerdalet 1997 , Peters andMarrasé 2000 ) and that turbulence may have effects on phytoplankton cells by increasing the diffusion mixing of nutrients to the cell surfaces (Estrada andBerdalet 1997 , Sullivan et al. 2003 ).Phytoplankton ecologists have long observed that turbulence levels in the water column impact phytoplankton communities in relation to the shape , size , and swimming capacity of phytoplankton species.Published 45 years ago, a concept known as 'Margalef's Mandala', categorized phytoplankton into groups based on nutrient availability and turbulence intensity.It sho w ed that diatoms are favoured by highly turbulent and nutrient-rich conditions, while dinoflagellates preferred the opposite conditions (Margalef 1978 ).This conceptualization has been verified in several marine systems and is now gener all y accepted in marine phytoplankton ecology.Yet, there is also substantial evidence indicating that turbulence impacts various factors crucial for diatom cell survival including: nutrient av ailability (Estr ada and Berdalet 1997, P ahlow et al. 1997, Dell'Aquila et al. 2017 ), settling velocity (Estrada andBerdalet 1997 , Ruiz et al. 2004 ), c hain structur e (Clarson et al. 2009, Amato et al. 2017, Dell'Aquila et al. 2017 ), gene expression (Amato et al. 2017 ), and interactions with grazers and diatom-diatom encounter r ates r elated to r epr oduction and c hain formation (Rothschild and Osborn 1988 ).
In spite of its potential impact, little is known about the effect of turbulence on the diatom Pseudo-nitzschia spp.This is a critical gap given that Pseudo-nitzschia spp.can form harmful algal blooms (Bates et al. 2018 ) and climate change is anticipated to modify turbulence in the oceans via two opposite processes: increase in the water column stratification due to the warming of the upper ocean, and increase in the frequency and intensity of extreme climatic events such as storms (IPCC 2023 ).There is, ther efor e, a need to e v aluate how and whether turbulence can influence Pseudo-nitzschia spp .'s growth and toxicity.
The factors triggering the blooms and toxicity [via the production of domoic acid (DA)] of Pseudo-nitzschia spp.are not completel y r esolv ed but temper atur e , salinity, nutrients , irradiance , photoperiod, association with bacteria, and upwelling e v ents hav e been found to influence its growth and toxicity (Bates et al. 1995, Bates 1998, Lelong et al. 2012, Trainer et al. 2012, 2018 ).Some of these factors can dir ectl y or indir ectl y inter act with turbulence (Estrada andBerdalet 1997 , Arnott et al. 2021 ).For example, by influencing the degree of mixing of phytoplankton cells within the water column, turbulence intensity impacts the cells access to the surface photic layer and exposure to light/dark conditions.
The hologenome concept (Zilber-Rosenberg and Rosenberg 2008 ) refers to all eukaryotic organisms, including unicellular algae.Pseudo-nizsc hia spp.liv e in association with a micr obiota composed of bacteria.Turbulence may also impact the relationships between Pseudo-nizschia spp.and their associated bacteria.The bacteria associated to diatoms can be classified into two categories: (i) the free-living bacteria, which are attracted by the organic matter exudated by phytoplankton cells and are motile, 'swimming' to w ar ds the cell in a zone called the phycosphere; and (ii) the epiphytic bacteria, whic h ar e attac hed to phytoplankton cells .T he phytoplankton-bacterial inter actions ar e governed by the production of metabolites by both communities, which can either be beneficial or harmful (e.g .Grossart 1999, Seymour et al. 2017 ).The bacteria associated with Pseudo-nitzschia spp.often belong to different classes such as Gammaproteobacteria (mainl y r epr esented by the genus Marinobacter and Alteromonas ), Alphaproteobacteria (mainl y r epr esented by Phaeobacter ) and Bacilli (e.g .Guannel et al. 2011, Lelong et al. 2012, Sison-Mangus et al. 2016 ).Because a part of the DA produced by toxic Pseudo-nizschia strains can be released to the media (Lelong 2012, Trainer et al. 2012 ), some r esearc hers hav e hypothesized that the Pseudonitzschia spp ./bacteria relationship may influence the production of DA by Pseudo-nitzschia spp.and would modulate the composition of the associated heter otr ophic bacterial comm unity (Bates et al. 1995, Guannel et al 2011, Lelong et al. 2012, Sison-Mangus et al 2016 ).Although, the potential effects of associated bacteria on Pseudo-nitzsc hia spp.gr owth and production of DA are not r esolv ed; it has been reported that DA production and growth of axenic cultures of Pseudo-nitzschia spp.are reduced in comparison to nonaxenic cultures (Lelong et al. 2012 and references therein).
Given all the abo ve , it can be hypothesized that turbulence could influence Pseudo-nitzschia spp.and that different levels of turbulence are likely to modify the relationship between Pseudonitzschia spp.and associated bacteria with potential consequences on the production of DA.To explore this, laboratory experiments were conducted by exposing two species of Pseudo-nitzschia ( Pseudo-nitzschia fraudulenta and Pseudo-nitzschia multiseries ), cultured under nonaxenic conditions, to five levels of turbulence .T he control of turbulence intensity was made with the Agiturb turbulence generation system, designed to generate a precise, quantifiable, and homogenous turbulent flow (Le Quiniou et al. 2022 ).Cell abundance, chain formation, and toxicity of the two Pseudonitzschia species were measured, and the composition of the associated epiphytic bacteria was determined b y metabar coding (16S RNA gene amplicon sequencing).

Pseudo-nitzschia strains and culture maintenance
Two nonaxenic Pseudo-nitzschia strains were used .Pseudo-nitzschia multiseries NWFSC713 was isolated from Puget Sound in the USA (pr ovided by V. Tr ainer, NOAA, Marine Biotoxins Pr ogr am, USA) and Pseudo-nitzschia fraudulenta PNfra 20-6 was isolated in the eastern English Channel (provided by J. Fauchot, UNICAEN, CNRS UMR 8067, BOREA, Fr ance).The cultur es wer e gr own in K/2 medium (Keller et al. 1987 ), at 15 • C, with a 12L:12D photoperiod and under an irradiance of 100 μmol photon m −2 s −1 .The autoclaved K/2 medium was prepared with natural seawater from the eastern English Channel aged in the dark for se v er al months before use .T he cultur es wer e gr o wn in or der to obtain large v olumes (5 l) needed for the experimental r equir ements.No turbulence was applied in the cultures.

Agiturb turbulence gener a tion system
The experiments were conducted with the Agiturb turbulence generation system (Le Quiniou et al. 2022 ).It is based on the 'Fourroll mill' system proposed in 1934 by Taylor, which generates a strain-dominated two-dimensional laminar flow using four rolls with contr a-r otating r ates (Taylor 1934 , Wer eley andGui 2003 ).
The Agiturb system has some differences: it is a cubic tank with a maxim um ca pacity of 38 'l filled with 15 l of medium, under which four agitators are placed.The four agitators are contra-rotating at the same velocity ( ), which can be changed from 100 r m −1 (revolutions per minute) to 900 r m −1 (Table 1 ).Energy is injected into the flow through the motion of four stirring bars (3.8 cm long with a diameter of 0.8 cm) activated by four magnetic stirrers (VELP MST Digital 5 l) situated at symmetric positions ( Fig. S1 ).The energy dissipation rate ( ε) in developed turbulent flows (i.e.statistically homogenous and isotropic; Pope 2000 ) is provided by the following equation: where L is the scale at which the energy is injected in the system, and is of the order of the distance between the two adjusted agitators ( Fig. S1 ).In this case, L = 16 .8 cm and ˜ u is the mean fluctuating velocity as, with K , the kinetic ener gy.These par ameters permit the scale of the smallest eddies to be calculated: η the K olmogoro v scale ( 3 ), where, ν = 1 . 1 x 10 −6 m 2 s −1 for T = 18 ± 2 • C , is the kinematic viscosity (Schmitt 2020 ).The Taylor scale λ ( 4 ) has no clear physical inter pr etation but is useful for the comparison between different types of turbulent flow via the Taylor-based Reynolds number ( Re λ ; 5 ).
From the above equations and the experimental pr ocedur e described by Le Quiniou et al. ( 2022 ) the c har acteristics of the differ ent le v els of turbulence wer e deriv ed.These le v els corr espond to different conditions in the aquatic envir onment.Muc h of the surface open ocean typically exhibits average dissipation rates of the order 10 −10 -10 −6 m 2 .s −3 (Barton et al. 2014 ).More energetic zones including tidal c hannels, fr onts, storms, and breaking wa ves , ma y gener ate v ery high dissipation rates of the order of 10 −5 -10 −4 m 2 .s −3 , while rates of the order of 10 −3 m 2 .s −3 correspond to storm conditions (Dell'aquila et al. 2017 ).The dissipation rate applied in our experiments varied from 10 −3 -10 −6 m 2 .s −3 .Zero turbulence condition acted as negative control r eferr ed her e as 'still condition', (Table 1 , Fig. S1 ).The le v el of turbulence is c har acterized by the Taylor-based Reynolds number ( Re λ ), the value of the kinetic energy K , and the dissipation r ate .The v alues of these thr ee par ameters as a function of the speed of rotation are known.The Taylor-based Reynolds number r eferr ed to as 'Re ynolds n umber' ( Re λ ) will be used hereafter (Table 1 ).

Experimental set-up and sampling
The experiments were performed in 38 l tanks ( Fig. S1 ).The five turbulence intensities were applied in triplicate.For each experiment nine Agiturb systems were used.For every strain the experiments lasted 3 days and were held during two consecutive weeks.In the first week (r eferr ed to her e as W1), the still condition Re λ = 0 , Re λ = 160 , and Re λ = 240 turbulence le v els wer e applied.In the second week (referred to here as W2), the still condition Re λ = 0 , Re λ = 130 , Re λ = 360 wer e a pplied.The tanks were filled with 15 l of 0.2 μm filtered and autoclaved seawater then K/2 medium were added in each tank so that diatoms were grown in nutrient replete conditions (targeted initial concentrations NaNO 3 = 28 μM, Na 2 Si O 3 .9H 2 O = 45 μM , KH 2 PO 4 = 18 μM ).The tanks were cov er ed with a tr anspar ent glass to minimize contamination from the air.The cell abundance in cultures (which were in exponential growth phase) were checked prior of each experiment, and the culture was added in each tank to an approximate abundance of 1000 cells m l −1 .This is r elativ el y high initial concentration since Pseudo-nitzschia blooms typically reach abundances of medium to high 10 6 cells l −1 (e.g Trainer et al. 2012, Bates et al. 2018 , and r efer ences ther ein) All the experiments were run in a thermoregulated laboratory in the same conditions as the ones used for culture maintenance (i.e. 15 • C, with a 12L:12D photoperiod and under an irradiance of 100 μmol photons m −2 .s −1 ).
The initial culture was maintained in exponential growth phase for the W2 experiment.At each time point ( T 0 , T 24 , T 48 , and T 72 h), 500 ml were sampled with a sterile tube from each tank and immediately subsampled for the measurements of Chl a, nutrients, cell abundance and chain formation.Chl a concentrations wer e measur ed by fluor ometry as described by Lorenzen ( 1966 ).Inorganic nutrient concentrations, nitrate (NO 3 − ), nitrite (NO 2 − ), phosphate (PO 4 3 − ), and orthosilicic acid (Si(OH) 4 ) were analyzed according to Aminot and Kérouel, ( 2004) with a SEAL AA3 HR c hemistry anal yzer.
Samples (10 ml) were fixed with glutaraldehyde solution (1% v/v).For diatom cell counts, a Nageotte counting chamber using a Zeiss Imager M2 (magnification 100x) was used.The number of cells of the chains was calculated by counting the number of cells in e v ery c hain up to a total of 100 cells count.A pr oxy of gr owth of diatoms was estimated for each tank and each time point as the ratio of the cell abundance at a given time point to abundance at T0 (e.g .N 48 / N 0 ).
Free-living bacterial and viral abundances were monitored by flo w c ytometry to ensur e that the r esults wer e not ske w ed b y an exceptional bacterial and/or viral proliferation during our experiments.For free-living heterotrophic bacteria and virus abundance, 2 ml samples were fixed with glutaraldehyde at a final concentr ation of 1%, stor ed at 4 • C for 40 min, flash frozen in liquid nitrogen, and then k e pt at −80 • C until analysis with a Cytoflex cytometer (Beckman Coulter).Counts for heterotrophic bacteria and virus-like particles (VLP) were made after staining with SYBR-Green based on their green fluorescence (Marie et al. 1999, Brussaard 2004 , r espectiv el y).Two heter otr ophic bacterial populations were discriminated, one with high fluorescence called 'high nucleic acid' (HNA) and one with low fluorescence called 'low nucleic acid' (Lebaron et al. 2001 ).For viruses, two populations of VLP could be distinguished based on their fluorescence intensity.HNA could potentially indicate active and fast growing bacteria (Lebaron et al. 2001 ), while high fluorescence viruses are thought to potentially be algal viruses rather than bacteriophages (Brussaard and Martinez 2008 ).
Cell abundance increased during the experiment for both species and in all turbulence conditions, ho w e v er, standard deviation dr amaticall y incr eased after 48 h (Fig. 1 ).Samples of T 48 were selected for DA measurements, metabarcoding, and metatr anscriptomic anal yses .T hese samples wer e c hosen because of the r elativ el y lo w er standar d de viation observ ed between r eplicates of the Pseudo-nitzschia abundance (Fig. 1 ) and cell abundance r atio compar ed to T 72 (Fig. 2 and Fig. S2 ), as well as, the fact that, the chains were formed between T24 and T48 h.
For total domoic acid (tDA) measurement 50 ml were sampled in each replicate.Samples were sonicated then acidified with formic acid and concentrated with a solid phase extraction column (Agilent Cartridge Bond Elut C18).The elution was done with a mix methanol/water (50/50 v/v) and k e pt at −80 • C until further anal yses.DA measur ements wer e performed using liquid c hr omatogr a phy coupled with mass spectrometry in tandem (LC/MS-MS) as described by Ayache et al. ( 2019 ).The cell specific DA calculated here is indicative and was obtained by normalizing the total D A (tD A, in pg m l −1 ) to the cell density for each sample ( cells m l −1 ).
For metabarcoding and metatranscriptomics analyses of each tank and at each time point, 200 ml were immediately filtered on sterile filtration devices.To favour the study of the epiphytic bacteria community, 2 μm Nuclepore filters (47 mm, Millipore, USA) were used, permitting as many free-living bacteria as possible to  The physical linkage between Pseudo-nitzschia and the sequenced bacteria was examined using scanning electron micr oscopy (SEM).A filtr ation was made on a 2 μm Isopore filters (25 mm, Millipore, USA) to eliminate the glutaraldehyde used for conserv ation.Then, samples wer e dehydr ated in a gr aded series of ethanol (50%, 75%, 90%, and 100%) for 30 min at each grade and in a final bath of hexamethyldisilazane. Finally, samples were coated with gold palladium before being observed with a SEM (Hitachi S-3200 N).

DN A extr action, 16S rRN A gene amplicon sequencing, and processing of sequences
DN A extraction w as performed for the T48 samples follo wing the AllPrep DN A/RN A kit (Qiagen, Hilden, Germany) following manufactur er's pr otocol.Metabar coding w as used to describe bacterial diversity.16S rRNA gene amplicon next generation sequencing library preparations and Illumina sequencing were conducted at Azenta Life Sciences (South Plainfield, NJ, USA).Sequencing libr ary was pr epar ed using the MetaVx™ 16S-EZ 16S rRNA gene amplicon libr ary pr epar ation kit (Azenta Life Sciences, South Plainfield, NJ, USA).The selected kit amplifies the V3 and V4 hypervariable genomic regions using the primer set of the 16S-EZ pro-tocol.Indexed ada pters wer e added to the ends of the 16S rRNA gene amplicons by limited cycle PCR.Then, DNA libraries were validated and quantified before loading.The pooled DNA libraries were loaded on an Illumina MiSeq instrument according to manufacturer's instructions (Illumina, San Diego, C A, USA).T he samples were sequenced using a 2 × 250 paired-end configuration.Demultiplexed 16S gene sequences (i.e .11 870 156 r eads) wer e processed with the R package D AD A2 (Callahan et al. 2016 ) in order to identify amplicon sequence variants (ASVs).The pipeline includes se v er al steps.First, the primers wer e r emov ed using the filtering parameters [i.e.maxN = 0, minLen = 200, maxEE (5,5), and truncLen (240, 240)].Then, identical sequences wer e der eplicated and an abundance was associated with each unique sequence.After, a parametric model was used to learn the error rate for each sequencing run to identify ASVs .T hen, forw ar d and r e v erse sequences wer e mer ged.Finall y, the c himeric sequences wer e r emoved with the remo veBimeraDeNo vo function (D AD A2), using the consensus method.A total of 7 884 165 r eads wer e r emaining after these steps, corresponding to 4437 ASVs.Taxonomic annotation was performed with the RDP Naive Bayesian Classifier using the SILVA r efer ence data base (release 138; Wang et al. 2007, Quast et al. 2013 ).Samples wer e r ar efied at 51 649 sequences (i.e .the lo w est number of sequences in a sample) with phyloseq package (v.1.42.0;McMurdie and Holmes 2013 ).All ASVs sequences were aligned in Geneious Prime (v.2023.0.4) using MUSCLE algorithm (v.5.1 ;Edgar 2004 ).A phylogenetic tree was then built based on this alignment with FastTree plugin using default parameters (v.2.1.11;Price et al. 2009 ).ASVs not assigned to procaryotes or assigned to mitochondria and c hlor oplasts wer e r emov ed as well as singletons leaving a total of 1999 ASVs and 1 807 715 reads in 35 samples.One replicate sample ( P. multiseries Re λ = 160 ) was removed due to the low quality of its reads.
Raw sequencing data have been submitted to the Short Read Arc hiv e under BioProject ID PRJNA980977.

Meta tr anscriptomic anal ysis
In the case of P. multiseries , where previous work about the DA production locus exists (Brunson et al. 2018 ), RNA extraction was performed at samples at 48 h at R e λ = 160 and in the 'still conditions' (R e λ = 0), in order to assess the ov er all system and the DA functional profile shifts under these treatment regimes, as follo ws: total RN A w as extr acted fr om flash-fr ozen 0.2-μm nucleopore filters containing, using the AllPrep DN A/RN A kit (Qiagen), along with DN A extractions.RN A sequencing w as performed b y Genewiz with the NEBNext Ultra II RNA Library Preparation Kit (New England Biolabs , Ips wich, MA, USA) for the library preparation and bacterial and eukaryotic rRNA depletion and the S2 chemistry kit at a NovaSeq 6000 instrument (Illumina) for generating 2 × 150 bp reads .T he reads were processed according to the SAMSA2 pipeline (Westr eic h et al. 2018 ).Within the pipeline context, Trimmomatic v0.36 (Bolger et al. 2014 ) was used for quality controlling/filtering the sequence reads using a sliding window of four bases of a minimum mean quality of Phr ed Q v alues of 15 as a 3 trimming cut-off and a minimum length of 70 for posttrimming read filtering cutoff value.PEAR v0.9.10 (Zhang et al. 2014 ) was then used for read-pair assembly with the default parameters .T he contigs derived from read-pairs were then screened with SortMeRNA v2.1 (K opylo va et al. 2012 ) for prokaryotic 16S and 23S rRN A, for eukary otic 18S and 28S rRNA, and for 5S and 5.8S rRNA gene sequence remnants .T he rRNA-free assembled readpairs were then contrasted with the Diamond v2.0.11BLASTx algorithm, using the default parameters and retaining the best hit, against the RefSeq and the SEED Subsystems databases as maintained by the SAMSA2 group with the databases further enriched for NCBI residing Pseudo-nitzschia associated sequences of the DA associated genes as suggested by Brunson et al. ( 2018 ).

Sta tistical Anal ysis
All statistical anal yses wer e performed in R version 4.2.2.Data visualizations were made with the R package ggplot2 (Wilkinson 2016 ).For bacteria alpha-diversity indices (Observed richness, inverse Simpson, and Shannon) were calculated with the phyloseq pac ka ge (McMurdie and Holmes 2013 ).
To illustrate β-diversity, a nonmetric multidimensional scaling (NMDS) or dination plot w as preformed based on weighted UniFrac metric (Lozupone and Knight 2005 ).With this algorithm, a distance matrix between bacterial communities based on the phylogenetical distances between the sequences of the samples was obtained.The weighted parameter was used to assess a weight of each sequence based on their relative abundance in the sample.A distance-based perm utational m ultiv ariate anal ysis of v ariance (PERMANOVA; Anderson 2001 ) was used to e v aluate the statistical significance of differences between group centroids.
The counts matrix derived from the RNA sequencing data was used for identifying differential expression analysis between the two tested conditions by performing a Fisher's exact test.

Cell a bundance, c hain formation, Chl a , and DA
Concentrations of nitrate (NO 3 − ), nitrite (NO 2 − ), phosphate (PO 4 3 − ), and silicate Si(OH) 4 were measured daily with all indicating decreases during the first 48 h.After 48 h, all nutrients were not depleted, with a minimum concentration for nitrogen of 6 .07 ± 2 .94 μM ( Table S1 ).Cell abundance increased for both str ains fr om the beginning until the end of the experiment (72 h, Fig. 1 ).The mean abundance of P. fraudulenta ranged from 1012 to 5740 cells ml −1 and was higher than the one of P. multiseries , which r anged fr om 700 to 1851 cells ml −1 .An incr ease in the standard de viations between r eplicates was observ ed ov er time for both strains in all turbulence conditions (Fig. 1 ).At 48 h the standard deviation between replicates was lower than for 72 h (Figs. 2 and Fig. S2 ).For both str ains, maxim um cell abundance at 48 h was found for intermediate turbulence le v els ( Re λ = 160 or 240, Fig. 2 ).Chl a concentrations were expressed in μg cell −1 .At 48 h, maximum Chl a per cell was found for intermediate turbulence at Re λ = 160 and Re λ = 240 for P. fraudulenta and P. multiseries , respectiv el y (Fig. 3 A and B).To note that, phaeopigments were undetectable in the growing cultures.At the beginning of the experiment, 92% of cells were single with this number dropping to 80% after 72 h of culture .T he chains observed at 72 h were relatively short with a mean 1 . 1 ± 0 .13 cell chai n −1 for P. fraudulenta and 1 .7 ± 0 .5 cell chain s −1 for P. multiseries .At 48 h, maxim um c hain formation was also found for intermediate turbulence ( Re λ = 160 or 240 ; Fig. 3 C and D).
tDA was measured at 48 h.No toxin was found in P. fraudulenta.In P. multiseries , toxin was found in all turbulence conditions.Concentr ations r anged fr om 11 . 1 to 211 .8 pg m l −1 .These tDA concentr ations wer e normalized r elativ e to the number of cells in eac h sample.Maxim um DA concentr ations in the tanks and DA normalized per cell were found for intermediate turbulences ( Re λ = 160 or 240 ).Ho w e v er, consider able standard deviation betw een replicates w as observed (i.e. in P. multiseries , Re λ = 160 , DA was 0 .008 ± 0 .005 pg cel l −1 , Fig. 3 E and F).

Bacterial community associated with Pseudo-nitzschia spp.
Observations of Pseudo-nitzschia under the SEM confirmed the presence of epiphytic bacteria on the phytoplankton cells, attached together with mucus and the presence of free-living bacteria remaining on the 2-μm filters ( Fig. S4A -D ).Sequencing of the 16S rRN A gene sho w ed se v er al differ ences between bacterial communities associated with P. multiseries and P. fraudulenta .Out of the total of 1999 ASVs detected, 1027 were only present in P. fraudulenta samples, 517 wer e onl y pr esent in P. multiseries samples, and 455 were shared between both strains ( Fig. S5A ).Richness was higher in the nontoxic P. fraudulenta samples (ranging from 61 to  S5A ).The highest variability in alpha diversity between replicates were observed in still and storm conditions ( Re λ = 0 and 360 ; Fig. S5C and D ).
In all samples, Pseudomonas , Pseudoalteromonas , and Marinobacter were the most abundant genera.Ho w ever, their relative abundances wer e differ ent.Pseudomonas dominated in P. fraudulenta cultures, while Marinobacter dominated in P. multiseries cultures.Bacillus (belonging to the phylum Firmicutes) was an important genus in P. multiseries (15.5%) but low in P. fraudulenta (0.3%; Fig. 4 A and B).The abundance of Bacillus in P. multiseries cultures was higher in the samples with the highest DA concentrations.( Fig. S6 ).
The r elativ e abundance of the five most abundant genera of each sample was plotted versus the turbulence intensity (Fig. 4 C).A high variability was observed between weeks and replicates, and thus, no clear trend could be established between turbulence intensity and bacterial community structure.
An NMDS plot indicated significant differences between the bacteria communities associated with P. fraudulenta and P. multiseries cultures (PERMANOVA, P < .05),but did not clearly cluster according to turbulence intensities (Fig. 5 ).Finally, to investigate the relationship between the level of turbulence and the observed v ariability between r eplicates, coefficients of v ariation of the r ead abundance were calculated for the 12 most abundant genus at each turbulence intensity.The lo w est variability was observed in intermediate turbulence with a mean C v = 55 % at Re λ = 160 .Maxim um v ariability was found for extreme conditions (Still and Storm) with r espectiv el y C v = 86% and C v = 109% ( Fig. S7 ).

mRN A anal ysis
A total sum of ∼26 M read-pairs were obtained from each sample (i.e. the 'still condition'; Re λ = 0; 15.6 M read-pairs) and the intermediate turbulence treatment (Re λ = 160; 10.6 M read-pairs) of the P. multiseries experimental series.Out of these, ∼ 61% were of high enough quality (after mer ging, r etaining the merged or first reads of each pair for avoiding double-counting, and removal of remaining ribosomal sequences) for the downstream analysis.
Comparison between the Re λ = 160 and the Re λ = 0 of the P. multiseries experiment sho w ed that the vast majority of the microbiome functional categories were, on av er a ge, downr egulated under turbulence conditions, whereas DA biosynthesis and photosynthesis were the only metabolic activities that were significantl y upr egulated (for cutoff v alues of 2 log 2 fold change and P < 0.05; Fig. S8A ).Out of the four genes of the suggested DA locus (Brunson et al. 2018 ), dabA and dabC sho w ed the highest log 2 fold changes ( Fig. S8B ), genes that code for a cyclase-like protein and an a-ketoglutarate (aKG)-de pendent dio xygenase, respectiv el y ( Fig. S8C ).

Discussion
Pr e vious studies dealing with the effect of turbulence on diatom growth used the 'still condition' and a single turbulent condition (Clarson et al. 2009, Amato et al. 2017, Dell'aquila et al. 2017 ).Howe v er, the pr esent study investigated, for the first time, the effect of a variable turbulence level (from Re λ = 0 to Re λ = 360 ) on se v er al variables including DA production of two species of the marine diatom Pseudo-nitzschia and their associated bacterial communities.The major findings of this w ork w ere that the cell abundance, DA pr oduction, c hain formation, and Chl a content of P. fraudulenta and P. multiseries were higher for intermediate turbulence .T his study also highlighted a higher richness of the bacterial community associated with the nontoxic strain of P. fraudulenta in comparison to the toxic strain of P. multiseries .Furthermore, these bacterial communities did not seem to be dir ectl y impacted by turbulence intensity.This absence of clear trend related to turbulence intensity could be also due to the high variability observed: (i) between replicates; (ii) between W1 and W2 for the same strain; and (iii) between strains.

Effect of turbulence intensity on Pseudo-nitzschia spp.
A major constraint when studying Pseudo-nitzschia spp. is the inter-and intr astr ain v ariability (e .g .T hessen et al. 2009 ) and is likely to be one of the many facets illustrating the difficulties en-countered when studying planktonic organisms, and in particular Pseudo-nitzsc hia , in labor atory cultur es (Shi et al. 2009, Lema et al. 2017 ).This high variability can explain why there is still today no clear consensus on the conditions triggering the HABs of Pseudonitzschia .
In our results, high variability was observed between replicates as well as between the 2 weeks of the experiment.Large v ariability between r e plicates might deri v e fr om the instability of Pseudo-nitzschia cells in culture, especially exacerbated by the large volume of culture, despite the attention paid to introduce the same inoculum concentration and the same growth stage, and the same controlled conditions of culture (light, nutrients, and temper atur e).Indeed, lar ge volumes (15 l) were used to have precise , quantifiable , and homogenous levels of turbulence .T he decr ease in v ariability between r eplicates at intermediate turbu- lences observed in this study has not been reported before and indicates better growth conditions ( Fig. S7 ).The highest cell abundances wer e observ ed for both str ains at intermediate turbulence intensities ( Re λ = 160 or 240 ), while minimum cell abundance was observed in low turbulence le v els and still conditions .T his is the first evidence that a diatom presented a 'dome-shape' response to turbulence as pr e viousl y described for other marine organisms (i.e .for zooplankton: Cury and Roy 1989, Sundby and Fossum 1990, MacKenzie et al. 1994, and Le Quiniou et al. 2022 ).The same pattern was found for the chain formation, with the longest chains found at Re λ = 160 for P. fraudulenta and Re λ = 240 for P. multiseries .As detailed by Dell'aquila et al. ( 2017), an increase in chain formation leads to an optimized surface-to-volume ratio and thus incr eases the pr obability of the cell to encounter a nutrient-rich zone when placed in a turbulent envir onment.Typicall y Pseudonitzsc hia forms r elativ el y long c hains in situ (e.g.Swan and Davidson 2007 ).The morphological trait of chain formation tends to disa ppear in cultur e (Smayda and Boleyn 1966 ).Two cell c hains ar e described in cultures before (e.g.Amato et al. 2017 ).
Inter estingl y, our r esults confirm that the formation of chains was higher in a turbulent environment than in a still condition in accordance with observations of Wadt et al. ( 2017), who reported a stim ulation of c hain formation with constant swirling of cultur es.Ho w e v er, the highest le v el of turbulence used in our experiments ( Re λ = 360 ) had a negative effect on both Pseudo-nitzschia strains.For example, the decrease in chain formation observed in storm conditions ( Re λ = 360 ) applied here has not been reported before.Two hypotheses could explain the chain formation decrease: (i) the high le v els of turbulence tend to break the chains; (ii) the concentrations of dissolved nutrients become more homogeneous in higher turbulence, leading to a loss of the adv anta ge associated with the chain formation.During the culture of P. fraudulenta and P. multiseries , an increase of Chl a concentration per cell was also observed in intermediate turbulence intensities ( Re λ = 160 or Re λ = 240 , r espectiv el y).This observ ation implies that v ery high turbulence intensity had a negative effect on these diatoms.Ho w e v er, it is important to remember that the experiments were run during two consecutive weeks (W1 and W2) for each strain with three turbulence intensities each week ( Re λ = 0 ; 160 ; 240 and Re λ = 0 ; 130 ; 360 , re psecti vely ).Furthermore , the a verage cell size of a diatom population decreases at each cell division during the v egetativ e phase .T he lo w er Chl a content observed in W2 could thus be simply explained by a smaller av er a ge cell size (e .g .J ewson 1992 ) but no size decrease was observed during our study between W1 and W2.
Regarding the production of the toxin DA, it should be noted that unlike P. multiseries , which has been shown in numerous studies to be capable of producing DA (e.g .Bates et al. 1991 ), the toxin production by P. fraudulenta is still debated (e.g .toxic for Tatters et al. 2012 ;and nontoxic for Teng et al. 2016 ).During our experiments, none of the P. fraudulenta samples had detectable le v els of DA.This result supports the finding of Dong et al. ( 2020 ), that P. fraudulenta produced DA only in the presence of grazers.In contrast, all the P. multiseries samples exhibited DA concentrations ( 73 ± 58 pg m l −1 ) in the range of the highest values found in the open ocean.On a transect in the Eastern Atlantic Ocean, Geuer et al. ( 2019 ) found a maxim um concentr ation of dissolved DA of 53 .9 pg m l −1 with a mean of 9 .9 pg m l −1 in the water column.It has been also highlighted that DA production was reduced in cultur e conditions compar ed to the pr oduction in the natural environment.Depending on the P. multiseries strain studied, DA content can vary from 3 × 10 −4 to 4 , 8 pg cel l −1 (Dong et al. 2020 ).
In our experiments, the highest DA concentration normalized per cell number was 0 .14 pg cel l −1 .
T he biosynthetic pathwa y of DA in volves four identified genes (Brunson et al. 2018 ).The occurrence (but not its expression) of the dabA gene has been r ecentl y r eported in two ( P. multistriata and P. delicatissima ) out of five cultures of Pseudo-nitzschia species isolated from the Adriatic Sea (Turk Dermastia et al. 2022 ), but the production of DA was detected only in P. multistriata .In our study, DA synthesis was further confirmed by metatranscriptomics analysis of selected samples from the same experiments where four of the known DA biosynthesis genes were highly expressed after 48 h from the beginning of the experiment at Re λ = 130 compared to the control in the P. multiseries tanks ( Fig. S8 ).According to recent r esearc h, the induction of DA biosynthesis was associated, in a species-specific manner, with growth phase (Sauvey et al. 2023 ), and with photosynthetic conditions (Brunson et al. 2018 ).Our results collectiv el y suggest that the turbulence (an abiotic str ess) related induction of the expression of the P. multiseries DA production locus is linked to an increase of photosynthetic activity in the total metatr anscriptome.DA pr oduction has been pr e viousl y deemed energy demanding (LeLong et al. 2012 ), and may possibly drive the observed upregulation of the photosynthetic genes.

Rela tionship betw een Pseudo-nitzschia spp . and the associated bacteria under a gradient of turbulence
The highest concentration of free-living bacteria measured was 7 .49 × 10 5 cells m l −1 .This is in the same range of the mean concentration of prokaryotic cells in the upper 200 m of the open ocean (e.g. 5 × 10 5 cells ml −1 ).The highest VLP concentration found during the experiment was 3 .1 × 10 5 ml −1 , which w as tw o orders of magnitude lower than what can normally be observed in a coastal environment.As a reference, in the English Channel, VLP concentr ation r anged fr om 0 . 1 to 5 .8 × 10 7 ml −1 ( 1 .6 ± 0 .9 × 10 7 ml −1 , 269 from samples collected across all seasons, 2018-2020; Christaki, unpublished data).These results suggest that the importance of free-living bacteria and viruses in the cultures remained minor compared to diatoms.Viruses will not be further discussed here.
The highest abundance of free-living bacteria in samples of both strains were observed in low turbulence conditions ( Re λ = terial growth and activity in turbulent conditions (e.g .Bergstedt et al. 2004 ).Due to their small size r anging fr om 0 . 2 to 1 μm , heter otr ophic bacteria are smaller than the K olmogoro v and the Batc helor scale, whic h corr esponds to the smallest heter ogeneity produce by turbulence .T hus , bacteria should not be affected by turbulence intensity.Ho w e v er, due to their motility, some bacteria are able to navigate through their environment and exploit small ephemer al patc hes of higher nutrient concentr ations (Taylor and Stocker 2012 ).
In this study, we focused on the epiphytic bacteria by sequencing the 16S rRNA gene of samples filtered on 2 μm filters to remove most fraction of the free-living bacteria (diameter ≈ 0 .5 μm ).Ho w e v er, as it can be seen on the images of diatom under the SEM, some free-living bacteria remained on the filter ( Fig. S4 ).Additionall y, the anal ysis of SSU gene sequences sho w ed that the nontoxic strain of P. fraudulenta presented more ASVs (1482) compared to the toxic strain of P. multiseries (972).This supports the observation made by Sison-Mangus et al. ( 2014 ) that when an algae produces toxin, the diversity of bacterial communities associated with it decreases .T his ma y be because the associated bacteria need to e volv e in a host-specific way to r esist or e v en benefit fr om the toxin and/or that nontoxic algae can interact with a more div erse r ange of bacteria.Suc h specific r elationships of phycopsher e bacteria have been reported for toxic and nontoxic dinoflagellates and remain to be elucidated for Pseudo-nitzschia spp.(Deng et al. 2023 ).
The same pattern was observed for all alpha diversity indices (Richness, Shannon, and Simpson), conv er ging to an increase in diversity at intermediate turbulence ( Re λ = 130 or 160 ; Fig. S5 ).The variation of alpha diversity of bacteria associated to phytoplankton r elativ e to turbulence le v els has not been assessed until now.While it is tempting to attribute this pattern to the possibility for bacteria with lo w er or higher turbulence pr efer ence to thriv e in those intermediate turbulence conditions, this hypothesis remains to be verified.
Important variability in bacterial community structure was observ ed between r eplicates, howe v er, this v ariability was higher for extreme conditions ('still' or 'storm'; Fig. S7 ).The variability between samples is discussed below.The samples were dominated by the phylum Proteobacteria , as it is usually the case in phytoplankton cultures (e.g .Kahla et al. 2021 ).Depending on the cultiv ated Pseudo-nitzsc hia str ains, differ ent gener a wer e dominant (Fig. 4 A and B).The genus Pseudomonas was found to be dominant in P. fraudulenta samples (40.2% of the whole bacterial community).This genus is known for its ability to occupy a wide range of habitats in water as in soils but has been reported as being inhibited by low le v els of DA (Stewart et al. 1998 ).This sensitivity could explain why Pseudomonas r epr esented onl y 8.8% of the bacterial community associated with P. multiseries .In P. multiseries samples, the dominant genus was Marinobacter, a marine bacteria commonly dominant in the phycosphere (Lupette et al. 2016 ).None of these taxa a ppear ed to hav e r elativ e abundances r elated with the turbulence intensity (Fig. 4 C).The lack of a clear effect on the different genus can be explained by the focus made here on the epiphytic comm unity.Attac hed bacteria pr obabl y r espond to the diatom variability rather than to the surrounding turbulence intensity.In addition, a small fraction of free-living bacteria remained on the 2 μm filters, which has probably amplified the variability in our results ( Fig. S4D ).This high variability can be clearly observed on the relative abundances of dominant genus in the replicates (Fig. 4 C) and is also reflected on the NMDS plot where bacterial communities were grouped mainly by strain rather than by turbulence le v el (Fig. 5 ).
The genus Bacillus found in P. multiseries and P. fraudulenta cultures, was the only re presentati ve of the phylum Firmicutes.Keeping in mind that taxonomic identification at the species le v el based on short sequences remains highly uncertain, it is worth noting that 88% of the sequences affiliated to the genus Bacillus were assigned to B .horikoshii .This bacterium is one of the endosymbiotic bacteria known for its production of a toxin used by their host: the tetrodotoxin (TTX; e.g .Tetrodontidae : puffer fish; Lu and Yi 2009 ).Inter estingl y, in our results, the r elativ e abundance of B. horikoshii was 12 times higher in the toxic cultures of P. multiseries than in the nontoxic cultures of P. fraudulenta .The relation observed between the relative abundance of Bacillus and the concentration of DA normalized to the number of prokaryotic cells is also intriguing and has not been reported before ( Fig. S6 ).The hypothesis that the potential toxic nature of Bacillus helps it to adapt its own metabolism to withstand its own toxin, resulting in its resistance to other toxins, as DA produced by P. multiseries , could be adv anced her e .T his adaptation could also explain its dominance in toxic environments that are harmful to more sensitive taxa.Ho w e v er, TTX was not measured here, which limits any further inter pr etation and/or hypothesis around the relationship between P. multiseries and Bacillus .Besides the potential importance of TTX, Bacillus is known to produce extracellular algicidal compounds against phytoplankton such as c y anobacteria, dinoflagellates and haptophytes of the genus Phaeocystis (e .g. Ma yali and Azam 2004 , Guan et al. 2014, Shao et al. 2022 ).Variation in bacterial communities may affect DA production by Pseudo-nitzschia (Lelong et al. 2014 ).To better c har acterize the r elationship between P. multiseries and Bacillus , it would be interesting to test the effect of TTX and the effect of Bacillus algicidal compounds on toxic Pseudo-nitzschia strains, and the effects of DA on the cultures of Bacillus.

Conclusions
Despite the difficulties associated with studying the diatom Pseudo-nitzsc hia spp ., gener al tr ends ar e beginning to emer ge after se v er al decades of r esearc h on this genus by the scientific community.This work contributes to this ongoing effort to understand the ecology of Pseudo-nitzschia spp ., by incor por ating a novel and significant aspect-the effect of turbulence.Indeed, this study provided the first evidence that the cell abundance of Pseudo-nitzschia , its tendency to form chains, its pigment content, and its toxin production, were higher at intermediate turbulence le v els .T his is the first evidence that a diatom presented a 'dome-shape' response to turbulence intensities as pr e viousl y advocated for zooplankton.In order to better understand mechanisms employed by Pseudonitzsc hia spp .to ada pt to the turbulence of its environment, future studies should focus in the collection and analysis of targeted metatr anscir ptomic data related to the expression and regulation of DA biosynthesis.Our results point to w ar ds the significance of abiotic stresses on DA production with photosynthetic pathways going hand-in-hand in this phenomenon.The bacterial communities associated with Pseudo-nitzschia spp .have also been studied, with a focus on epiphytic bacteria.Corr obor ating with pr ecedent r esearc h, toxic str ains hav e a less div erse associated bacterial comm unity compar ed to nontoxic str ains.Furthermor e, the study of associated bacteria has highlighted the genus Bacillus (and potentially the species B. horikoshii ) as being closely associated with the concentrations of DA produced by P. multiseries .This relationship warrants further in-depth investigation to better understand the role of the metabolites produced by one species that either pr omote or r estrict the gr owth of the other.The major difficulty in this study was the high le v els of v ariations observ ed in the cell abundance of Pseudo-nitzschia as well as in the associated bacterial communities.Ho w ever, this variability appears to be reducible by a better understanding of the optim um gr owth conditions of the strains studied.One of the effects of turbulence, which is not taken into account in this study but cannot be neglected in the objective of modelling and predicting HAB events, is the vertical mixing on decameter-scale depth.Indeed, this mixing will result in the resurfacing of cells adapted to deeper environments (darker and colder) and vice v ersa.Consequentl y, these cells would be subjected to stressful conditions more frequently than cells adapted to less turbulent environments (Falkowski 1983 ).

Figure 1 .
Figure 1.Cell abundance of (A) Pseudo-nitzschia fraudulenta and (B) Pseudo-nitzschia multiseries .Black lines represent the mean and grey zone the standard deviation of all replicates and levels of turbulence for each time point.Note scale difference.

Figure 3 .
Figure 3. Content in Chl a per cell at 48 h of (A and B), mean number of cells in chains (C and D), and DA concentrations and DA normalized per cell at 48 h (E and F).No DA was found in P. fraudulenta samples.Vertical bars r epr esent the standard deviations between the different replicates.

Figure 4 .
Figure 4. (A and B) Voronoi plot representing the relative abundance, given in percentage of the most abundant bacterial genera in (A) P. fraudulenta and in (B) P. multiseries cultures at 48 h.Only the genera shared between both cultured strains are represented here.(C) The five most abundant genera of each sample at 48 h for each week of experiment versus turbulence intensity.Each bar represents a replicate.Re λ = 160 for P. multiseries only two replicates because of the low the quality reads of one replicate.

Figure 5 .
Figure 5. NMDS based on Unifrac Weighted distances for bacteria.All turbulence le v els, differ ent colours r epr esent differ ent weeks of experiment, each point represents a sample.treatment.

Table 1 .
Turbulence intensities used during the experiments.